Signal Processing 4 the Networked Battlespace

Sensors have for a long time played a vital role in battle awareness for all our armed forces, ranging from advanced imaging technologies, such as radar and sonar to acoustic and the electronic surveillance. Sensors are the "eyes and ears" of the military providing tactical information and assisting in the identification and assessment of threats. Integral in achieving these goals is signal processing. Indeed, through modern signal processing we have seen the basic radar transformed into a highly sophisticated sensing system with waveform agility and adaptive beam patterns, capable of high resolution imaging, and the detection and discrimination of multiple moving targets.
Today, the modern defence world aspires to a network of interconnected sensors providing persistent and wide area surveillance of scenes of interest. This requires the collection, dissemination and fusion of data from a range of sensors of widely varying complexity and scale - from satellite imaging to mobile phones. In order to achieve such interconnected sensing, and to avoid the dangers of data overload, it is necessary to re-examine the full signal processing chain from sensor to final decision.
The need to reconcile the use of more computationally demanding algorithms and the potential massive increase in data with fundamental resource limitations, both in terms of computation and bandwidth, provides new mathematical and computational challenges. This has led in recent years to the exploration of a number of new techniques, such as, compressed sensing, adaptive sensor management and distributed processing techniques to minimize the amount of data that is acquired or transmitted through the sensor network while maximizing its relevance. While there have been a number of targeted research programs to explore these new ideas, such as the USs "Integrated Sensing and Processing" program and their "Analog to Information" program, this field is still generally in its infancy.
This project will study the processing of multi-sensor systems in a coherent programme of work, from efficient sampling, through distributed data processing and fusion, to efficient implementations. Underpinning all this work, we will investigate the significant issues with implementing complex algorithms on small, lighter and lower power computing platforms. Exemplar challenges will be used throughout the project covering all major sensing domains - Radar/radio frequency, Sonar/acoustics, and electro-optics/infrared - to demonstrate the performance of the innovations we develop.

The impact of the proposed research will be manifold. The military have an aspiration of a network of interconnected sensors providing persistent and wide area surveillance of scenes of interest, and to this end have invested heavily in a Network Enabled Capability (NEC) of satellites and ground stations linking to unmanned vehicles, platforms and drones. Such a capability will provide an important operational advantage to our armed forces. The research in this project, examining the acquisition and processing of information from multi-sensor systems in an integrated manner, will help them realize this capability.
Furthermore, as the research project aims to work closely with the UK defence industries there is likely to be a significant economic benefit. A successful project will translate into greater technology pull through of our research ideas into the commercial sector and will help our defence companies remain at the leading edge in the international defence market.
Beyond the defence sector this work will impact on many other disciplines that will benefit from the development of improved sensing systems, with reduced computation and power costs, including: robotics, astronomy, remote sensing and medicine.
Finally our proposed research sits right at the heart of the EPSRC intelligent information infrastructure (TI3) vision to "intelligently manage massive amounts of data, ensure efficient communications and exploit the content and information that will be available." It will therefore impact on a range of related academic areas from mathematics and statistics, through computer science to the engineering of novel sensing systems in areas such as surveillance and medicine.